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start_time <- Sys.time()
output_name <- "CA121_WT_GA_Metal_analysis"

control_ID <- "CA121 WT_dH2O"
protein_filter <- NA
ligand_filter <- NA

analysis_path <- './Analysis'

rawData_path <- './Raw'

# library('svglite')
library(ggplot2)
library(ggpubr)

Attaching package: ‘ggpubr’

The following object is masked from ‘package:TSAR’:

    get_legend

library(readxl)
library(stringr)
library(tidyr)

Attaching package: ‘tidyr’

The following object is masked from ‘package:testthat’:

    matches

library(dplyr)

Attaching package: ‘dplyr’

The following object is masked from ‘package:testthat’:

    matches

The following objects are masked from ‘package:stats’:

    filter, lag

The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union

library(readr)

Attaching package: ‘readr’

The following object is masked from ‘package:TSAR’:

    write_file

The following objects are masked from ‘package:testthat’:

    edition_get, local_edition

library(mgcv)

Loading required package: nlme

Attaching package: ‘nlme’

The following object is masked from ‘package:dplyr’:

    collapse

This is mgcv 1.9-1. For overview type 'help("mgcv-package")'.

library(TSAR)

nAnalysis<- length(dir(path = analysis_path)) 
nRawData <- length(dir(path = rawData_path)) 

tsa_df <- merge_TSA(
    analysis_file_path = dir(path = analysis_path,
                             full.names = TRUE), 
    raw_data_path = dir(path = rawData_path,
                             full.names = TRUE)
)
tsa_df

normalized_tsa_df <- normalize_fluorescence(tsa_data = tsa_df)
head(normalized_tsa_df)

library('svglite')

# names(normalized_tsa_df)[names(normalized_tsa_df) == "RFU"] <- "Normalized"
# TSA_compare_plotx(normalized_tsa_df,y = "RFU",control_condition = control_ID,show_Tm = T)
TSA_compare_plot(normalized_tsa_df, control_condition = control_ID,
                           y = "RFU", smoother = "beta",
                           beta_shape = 4, beta_knots_frac = 0.008)

$`CA121 WT_PF74`

$`CA121 WT_Gallic acid`

$`CA121 WT_FeCl3 + Gallic acid`

$`CA121 WT_CuCl2 + Gallic acid`

$`CA121 WT_CaCl2 + Gallic acid`

$`CA121 WT_MgCl2 + Gallic acid`

$`CA121 WT_NaCl + Gallic acid`

$`CA121 WT_FeCl3`

$`CA121 WT_CuCl2`

$`CA121 WT_CaCl2`

$`CA121 WT_MgCl2`

$`CA121 WT_NaCl`

$`Control: CA121 WT_dH2O`

# uncomment the following for plots generation


# for (nm in names(plots)) {
#   # If your list contains NULLs (e.g. for unused slots), skip those:
#   if (is.null(plots[[nm]])) next
# 
#   # Construct filename: e.g., "plot_ConditionA.svg"
#   fname <- paste0("plot_", gsub("[^A-Za-z0-9_-]", "_", nm), ".svg")
#   ggsave(filename = fname, plot = plots[[nm]], width = 6, height = 4, units = "in", device = "svg")
# }

# source("../R/tsa_curves.R")
for(i in 1:condition_IDs(normalized_tsa_df, n = TRUE)) {
    cond_i <- condition_IDs(normalized_tsa_df)[i]
    subset_i <- normalized_tsa_df$condition_ID == cond_i
    subset_i <- normalized_tsa_df[subset_i, ]
    # plot_i <- TSA_wells_plot(subset_i)
    plot_i <-  TSA_wells_plot(subset_i, y = "RFU", smooth = TRUE,
               smoother = "beta", beta_shape = 4, beta_knots_frac = 0.008)
    
    print(plot_i)
    
    # uncomment the following for plots generation
    # Create a filename for each condition
    # svg_file <- paste0("tsa_plot_", gsub("[^A-Za-z0-9_-]", "_", cond_i), ".svg")
  
    # Save the plot as SVG
    # ggplot2::ggsave(filename = svg_file, plot = plot_i[[1]], device = "svg", width = 6, height = 4, units = "in")
}

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# 1) Compare L2 (RMSE) across GAM and a small Beta grid
# source("../R/averageTSA.R")
res_L2 <- TSA_smoother_diagnostics(
  normalized_tsa_df,
  y = "RFU",
  metric = "L2",
  # beta_shapes = c(2, 4),
  # beta_fracs  = c(0.008, 0.02)
)

mean L2 error of gam method: 0.02851
mean L2 error of beta(a=4, frac=0.008): 0.01985

beta(a=4, frac=0.008) has lowest error and highest accuracy.

res_L2_df <- as.data.frame(res_L2)
res_L2_df

NA

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